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fonnesbeck/Parallel.ipynb

Created February 5, 2014 16:02
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Parallel.ipynb
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{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.parallel import Client\n",
"\n",
"cli = Client()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2,
"trusted": true
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create `DirectView` on engines"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dview = cli[:]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4,
"trusted": true
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Activate the `DirectView` to enable parallel magic commands."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dview.activate()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5,
"trusted": true
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Setup"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pandas import Series, DataFrame\n",
"import numpy as np\n",
"from am4fmd import fmdoutbreak, distances as dist"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14,
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"max_days = 150\n",
"\n",
"# Parameter dictionary\n",
"params = {\n",
"'n_feedlots': 30,\n",
"'mean_size': 100, # mean of poisson dist\n",
"\n",
"# Geographic parameters\n",
"'x_range': [0, 48*3],\n",
"'y_range': [0, 48*3], \n",
"\n",
"# Column vectors of susceptibility and transmissibility, each column is species type\n",
"'susceptibility': 0.1, # susceptibility per animal\n",
"'transmissibility': 15.4, # transmissibility per animal (Sellers... ?)\n",
"\n",
"# Times after infection\n",
"'tau_incubate': 4, # incubation period\n",
"'tau_detect': 9, # time until detection\n",
"'tau_cull': [1, 4], # culling time\n",
"\n",
"# Kernel parameters\n",
"'kernel_type': 'power',\n",
"'kernel_scalar': -4,\n",
"'kernel_power': -10\n",
"}\n",
"\n",
"# Generate the index cases\n",
"params['index_cases'] = [0]\n",
"params['n_index_cases'] = len(params['index_cases'])\n",
"\n",
"# Geography\n",
"params['xy'] = np.random.randint(np.diff(params['x_range'])[0], size = params['n_feedlots']*2).reshape([params['n_feedlots'], 2])\n",
"\n",
"# Create the distance matrix between all farms\n",
"params['D'] = np.matrix(dist.EuclideanDistance( params['xy'] ))\n",
"\n",
"# Codes for feedlot states, as used in 'infection_status' vector\n",
"params[\"states\"] = {\n",
"'susceptible':-1,\n",
"'culled':-2\n",
"}\n",
"\n",
"# Time when culling begins for each feedlot\n",
"# this is a uniform random integer: tau_detect + U(tau_cull[0], tau_cull[1])\n",
"params['cull_time'] = params['tau_detect'] + params['tau_cull'][0] + np.random.randint(params['tau_cull'][1], size = params['n_feedlots'])\n",
"params['cull_rate'] = np.array([40]*params['n_feedlots'])\n",
"\n",
"#########################\n",
"# CONTROL #\n",
"#########################\n",
"\n",
"# Set the time horizons to allow control to change\n",
"control_horizons = [7] + 3*np.arange(max_days/3)\n",
"control_horizons = np.insert(control_horizons, 0, 3)\n",
"\n",
"control_params = {\n",
"'horizons': control_horizons,\n",
"# Control measures\n",
"'control': \n",
"{\n",
"0: 'No control',\n",
"1: 'Stamping out IP',\n",
"2: 'Protective vaccination',\n",
"3: 'Suppressive vaccination'\n",
"},\n",
"# Keys are vaccine delivery horizons, values are dose sizes being delivered. \n",
"'vaccine_horizons':\n",
"{\n",
"7: 3000,\n",
"14: 5000,\n",
"21: 5000,\n",
"28: 5000,\n",
"35: 5000,\n",
"42: 2000\n",
"}\n",
"}\n",
"\n",
"control_params['inner_radius'] = 10\n",
"control_params['outer_radius'] = 20\n",
"control_params['tau_vaccination'] = 50\n",
"\n",
"# Create a dictionary of control measures (set all to 0, no control)\n",
"# keys are time horizons, values are control measure to be taken at that time horizon. \n",
"control = dict.fromkeys(control_horizons, [1, 2]) # stamping out and protective vaccination\n",
"control[3] = [1] # Only stamping-out at t=3 (no vaccine has arrived yet)\n",
"control_params['control_regime'] = control\n",
"\n",
"# Control measures \n",
"params['vaccination_rate'] = np.array([40]*params['n_feedlots']) # daily vaccination rate\n",
"\n",
"# Generate the number of animals in each feedlot\n",
"params['starting_N'] = np.random.poisson(params['mean_size'], params['n_feedlots'])\n",
"control_params['vacc_alloc_rule'] = 'size'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15,
"trusted": true
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run simulation on cluster"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"runs = dview.apply(fmdoutbreak.fmdoutbreak, params, control_params, max_days)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25,
"trusted": true
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retrieve results as dict"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"results = runs.get_dict()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32,
"trusted": true
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shape of a single output"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"results[0].shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"text": [
"(1020, 66)"
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}
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"prompt_number": 49,
"trusted": true
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `concat` to turn them into a `DataFrame`. Pandas will use the dict key as a hierarchical index, so we can tell the runs apart."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"frame_simulation = pd.concat(results)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45,
"trusted": true
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shape of combined results"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"frame_simulation.shape"
],
"language": "python",
"metadata": {},
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"(4500, 66)"
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"frame_simulation.head()"
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